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AI Engineering Team Manager
Parallel Wireless. Lead the AI team and own its technical direction, roadmap, and execution .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading AI teams, driving AI/ML initiatives from concept to production, and establishing engineering practices for model lifecycle management. Proficient in evaluating AI opportunities and delivering measurable impact through scalable solutions in complex technology domains.
Highest-signal resume keywords
AI/ML Engineering LeadershipProduction Deployment of ML SolutionsPython and PyTorch ProficiencyExperience in Telecom and RANKnowledge of 4G/5G Technologies
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI TechnologiesMachine LearningLarge Language ModelsModel Evaluation MethodologiesMLOps/LLMOpsScalable Solutions DevelopmentTechnical Decision-MakingPerformance ManagementData AnalysisAI Assistant Development
Soft Skills
Team BuildingMentoringCollaborationCommunicationPerformance Management
Tools & Technologies
Vector DatabasesAI Development FrameworksMonitoring ToolsEvaluation ToolsAutomation Tools
Certifications & Qualifications
B.Sc. in Computer ScienceM.Sc. in Electrical Engineering
Industry Keywords
TelecomWireless CommunicationsReal-Time SystemsNetwork ArchitectureOperational Efficiency
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- Lead the AI team and own its technical direction, roadmap, and execution
- Drive AI/ML initiatives from concept and prototyping through production deployment and ongoing optimization
- Define clear success metrics and ensure AI solutions deliver measurable product and business impact
- Lead architecture and technical decision-making around AI models, platforms, tools, and data
- Build and grow a multidisciplinary team, including recruitment, mentoring, performance management, and career development
- Establish engineering practices around evaluation, scalability, reliability, monitoring, and model lifecycle management
- Collaborate with Product Management, Systems Engineering, and R&D teams across Israel, India, and the US
- Partner with RAN and domain experts to identify opportunities for AI to improve network performance, automation, and operational efficiency
- Support customer-facing discussions, trials, and proofs of concept where relevant
Requirements
What you’ll need- 3+ years of experience leading engineering teams, including people management, hiring, and delivery
- 8+ years of experience in software, systems, or AI/ML engineering
- Proven experience delivering ML or LLM-based solutions into production, with measurable impact and ownership from development through deployment
- Strong understanding of modern AI technologies and architectures, including LLMs, RAG, AI agents, tool calling, evaluation methodologies, and MLOps/LLMOps
- Hands-on familiarity with Python, PyTorch, vector databases, and modern AI development frameworks
- Ability to evaluate AI opportunities, define technical approaches, and translate them into practical, scalable solutions
- Experience building and developing high-performing technical teams
- Experience working in a global, cross-functional R&D environment
- Fluent English, written and spoken
- Experience in telecom, RAN, wireless communications, real-time systems, or another complex technology domain
- Knowledge of 4G/5G technologies and network architecture
- Experience applying ML/AI to network, operational, or time-series data
- Experience developing AI assistants, agentic systems, or intelligent automation solutions
- Experience delivering products to enterprise or telecom customers with high requirements for reliability and performance
- B.Sc. or M.Sc. in Computer Science, Electrical Engineering, or a related field
Benefits
Comp & perks- Diversity and equality of opportunity
- Inclusive and diverse teams
- Innovation, flexibility, and sustainability-focused work environment